PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Boschetti, Mirco; Busetto, Lorenzo; Manfron, Giacinto; Laborte, Alice G.; Asilo, Sonia; Pazhanivelan, S.; Nelson, Andrew

doi:10.1016/j.rse.2017.03.029

Cited by 111 publications

(110 citation statements)

References 45 publications

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“…These preliminary results suggest that a very frequent time series of LAI at high resolution can be obtained from a multi-sensor approach to better outline rice-growing behavior. The combined curves of LAI could be exploited for: (i) the retrieval of phenological stages for crop modeling purposes [51][52][53]; (ii) deriving multitemporal training sets for mapping purposes [54]; or (iii) monitoring vegetation production by computing the area under the curve [55]. …”

Section: Spatial-temporal Consistency Between Sentinel-2a and Landsatmentioning

confidence: 99%

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

et al. 2017

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This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R 2 > 0.93) and good accuracies (RMSE < 0.83, rRMSE m < 23.6% and rRMSE r < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

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Section: Spatial-temporal Consistency Between Sentinel-2a and Landsatmentioning

confidence: 99%

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

et al. 2017

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“…Over the last three decades, different methods for rice mapping and monitoring have been developed using remote sensing data (McCloy et al 1987;Fang et al 1998;. The spatial extents of these studies range from experimental plots to continental scales and employ unsupervised, supervised, rule-based, and/or time series algorithms (Boschetti et al 2017;. The Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) constellations have been the most widely used because the spectral information they record is particularly suitable for agricultural characteristics (Okamoto 1999;Whitcraft et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…MODIS images have been used much more extensively in agricultural and rice monitoring applications at larger regional scales because of the faster re-visit time (~1 day) and relatively smaller datasets resulting from its lower resolution (BeckerReshef et al 2010;Boschetti et al 2017;Duveiler et al 2015;Xiao et al 2005Xiao et al , 2006Zhang et al 2017). also combined MODIS time series images with SAR active sensor data for rice monitoring, exemplifying new initiatives and novel techniques becoming available with the advent of free access to remotely sensed datasets, and improved expert understanding of regional rice production systems.…”

Section: Introductionmentioning

confidence: 99%

“…This is especially challenging when trying to maintain automation and minimizing expert user inputs. Boschetti et al (2017) showed how to use expert knowledge of temporal growing season information and rice phenology to improve accuracy in rice mapping using their automated PhenoRice algorithm. A number of other studies have mapped rice, even at national and sub-continental scales Zhang et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The predominant limitation was the large computational power required to analyze high resolution imagery across large land areas and over longer periods of time. For example, while much MODIS imagery has been successfully and widely used to map rice across large areas through time (Boschetti et al 2017;Xiao et al 2005Xiao et al , 2006Zhang et al 2017), the resolution is only at 250 m, which limits analysis to homogenous landscapes with little fragmentation. However, rice production often occurs in heterogeneous, fragmented landscapes, particularly in places such as Bangladesh where most farmers are small holders with an average farm size of 0.24 hectares (Rapsomanikis 2015).…”

Section: Introductionmentioning

confidence: 99%

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Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

Shew

Ghosh

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km 2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.

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Predicting rice phenology and optimal sowing dates in temperate regions using machine learning

et al. 2023

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Crop phenology modeling often involves determining variety‐specific growing degree day thresholds, or parameterizing mechanistic crop models. In this work, we used machine learning methods to develop models that provide daily predictions of the probability that rice (Oryza sativa) crops had reached the panicle initiation and flowering growth stages. These per‐date classifications were summarized into per‐paddock growth stage transition dates, which were then compared with field‐sampled reference data, encompassing 15 rice varieties, 10 years, and 380 sites. Leave‐one‐year‐out cross validation was used to provide realistic estimates of model errors. Compared with more complex and computationally intensive algorithms, logistic regression produced competitive results (mean cross‐season validation RMSE 3.9 and 5.2 days for panicle initiation and flowering, respectively). Logistic regression had additional advantages: providing confidence of growth stage predictions at each date (as it is a probabilistic algorithm), and straightforward explainability (as model parameters directly indicated how the various input variables contributed to growth stage predictions). Input variables included accumulated weather, rice variety, and sowing methods. The models were applied to forecasting phenology transition dates of the rice crops planted throughout the Murray and Murrumbidgee valleys. In addition, recommendations for optimal sowing dates were developed, using simulations involving more than 40 years of weather data, with the goal of minimizing the risk of cold‐temperatures during the microspore growth phase, which can severely degrade yield in temperate rice growing regions.

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PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Cited by 111 publications

References 45 publications

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

Predicting rice phenology and optimal sowing dates in temperate regions using machine learning

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